Decentralized AI Healthcare

Decentralized AI Healthcare
Privacy-preserving AI healthcare assistant powered by Bittensor for secure diagnosis, triage, and health insights.

Decentralized AI Healthcare is a privacy-first platform that leverages Bittensor's decentralized network to enable secure, collaborative medical AI. Patients and providers can interact with AI for diagnosis, triage, and health insights without compromising data privacy.

Key features:
- Federated learning on Bittensor subnet — AI models are trained locally, data never leaves the node
- AI-powered diagnosis with confidence scoring and medical advice
- Medical data anonymization for HIPAA-compliant data handling
- On-chain audit trail for full transparency and accountability
- Token-based incentive system ($TAO) for miners and validators

The platform addresses critical challenges in healthcare: data silos, privacy regulations, lack of incentives for data sharing, and centralized AI opacity. By combining federated learning with blockchain incentive mechanisms, we enable healthcare AI innovation without sacrificing patient privacy.

During the hackathon, we built the following from scratch:

1. Designed the full architecture for a privacy-preserving AI healthcare platform on Bittensor subnet, including federated learning workflow, miner/validator roles, and reward mechanisms.

2. Developed a working MVP backend API using FastAPI with:

- POST /diagnose endpoint for AI-powered medical diagnosis

- Patient data models with Pydantic validation

- AI diagnosis module with confidence scoring and medical advice

3. Integrated Bittensor subnet design for decentralized AI training — defining how miners train models locally and validators assess quality, with $TAO token reward distribution.

4. Implemented medical data anonymization layer using the Cryptography library for privacy-preserving data handling.

5. Created comprehensive documentation including project overview, pitch deck, demo video script, and visual guide.

6. Defined the reward formula for fair incentive distribution between miners and validators.

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